Classification of digital modulation schemes using linear and nonlinear classifiers

Approved for public release; distribution is unlimited === The potential benefits of automated detection of digital modulation types have made it a continuing topic of research for many years. Commercial systems could be made more interoperable and military sensors could send demodulated products...

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Main Author: Geisinger, Nathan P.
Other Authors: Fargues, Monique P.
Published: Monterey, California. Naval Postgraduate School 2012
Online Access:http://hdl.handle.net/10945/5445
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-54452015-08-06T16:02:20Z Classification of digital modulation schemes using linear and nonlinear classifiers Geisinger, Nathan P. Fargues, Monique P. Cristi, Roberto Robertson, Ralph C. Naval Postgraduate School (U.S.) Approved for public release; distribution is unlimited The potential benefits of automated detection of digital modulation types have made it a continuing topic of research for many years. Commercial systems could be made more interoperable and military sensors could send demodulated products for analysis, to name just two. Noisy channels and multipath fading environments continue to make this a challenging problem. This thesis applies classification algorithms that have been used in other applications. Nine different digital modulation schemes are considered. The criteria for selecting higher-ordered moments and cumulants as features for discrimination are discussed. An overview of the classification algorithms considered is provided, as well as the statistical models for noisy channels. Results show that the scheme proposed here works well in AWGN channels and in moderate fading conditions. 2012-03-14T17:45:26Z 2012-03-14T17:45:26Z 2010-03 Thesis http://hdl.handle.net/10945/5445 609686559 This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted. Monterey, California. Naval Postgraduate School
collection NDLTD
sources NDLTD
description Approved for public release; distribution is unlimited === The potential benefits of automated detection of digital modulation types have made it a continuing topic of research for many years. Commercial systems could be made more interoperable and military sensors could send demodulated products for analysis, to name just two. Noisy channels and multipath fading environments continue to make this a challenging problem. This thesis applies classification algorithms that have been used in other applications. Nine different digital modulation schemes are considered. The criteria for selecting higher-ordered moments and cumulants as features for discrimination are discussed. An overview of the classification algorithms considered is provided, as well as the statistical models for noisy channels. Results show that the scheme proposed here works well in AWGN channels and in moderate fading conditions.
author2 Fargues, Monique P.
author_facet Fargues, Monique P.
Geisinger, Nathan P.
author Geisinger, Nathan P.
spellingShingle Geisinger, Nathan P.
Classification of digital modulation schemes using linear and nonlinear classifiers
author_sort Geisinger, Nathan P.
title Classification of digital modulation schemes using linear and nonlinear classifiers
title_short Classification of digital modulation schemes using linear and nonlinear classifiers
title_full Classification of digital modulation schemes using linear and nonlinear classifiers
title_fullStr Classification of digital modulation schemes using linear and nonlinear classifiers
title_full_unstemmed Classification of digital modulation schemes using linear and nonlinear classifiers
title_sort classification of digital modulation schemes using linear and nonlinear classifiers
publisher Monterey, California. Naval Postgraduate School
publishDate 2012
url http://hdl.handle.net/10945/5445
work_keys_str_mv AT geisingernathanp classificationofdigitalmodulationschemesusinglinearandnonlinearclassifiers
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